
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
		from cookiecutter.main import cookiecutter

		lowerCamelCase__						=				True
except ImportError:
		lowerCamelCase__						=				False

lowerCamelCase__						=				logging.get_logger(__name__)  # pylint: disable=invalid-name


def 				__A(lowerCAmelCase			)		->					List[Any]:

		"""simple docstring"""






		return AddNewModelCommand(args.testing    ,	args.testing_file    ,	path=args.path			)







class 							lowerCAmelCase__   ( _A						):

						@staticmethod
						def 			A_					(      a					)       ->   List[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						parser.add_parser("""add-new-model"""					)
								add_new_model_parser.add_argument("""--testing"""							,				action="""store_true"""							,				help="""If in testing mode."""					)
								add_new_model_parser.add_argument("""--testing_file"""							,				type=a_							,				help="""Configuration file on which to run."""					)
								add_new_model_parser.add_argument(
								    """--path"""							,				type=a_							,				help="""Path to cookiecutter. Should only be used for testing purposes."""					)
								add_new_model_parser.set_defaults(func=a_					)

						def __init__(      self							,				a							,				a							,				a=None							,				*a					)       ->   Union[str, Any]:


								'''simple docstring'''

								_UpperCamelCase         =						testing
								_UpperCamelCase         =						testing_file
								_UpperCamelCase         =						path







						def 			A_					(      self					)       ->   List[str]:


								'''simple docstring'''

								warnings.warn(
								    """The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. """
								    """It is not actively maintained anymore, so might give a result that won't pass all tests and quality """
								    """checks, you should use `transformers-cli add-new-model-like` instead."""					)
								if not _has_cookiecutter:
										raise ImportError(
										    """Model creation dependencies are required to use the `add_new_model` command. Install them by running """
										    """the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n"""					)
								# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
								_UpperCamelCase         =						[directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
								if len(a_					) > 0:
										raise ValueError(
										    """Several directories starting with `cookiecutter-template-` in current working directory. """
										    """Please clean your directory by removing all folders starting with `cookiecutter-template-` or """
										    """change your working directory."""					)

								_UpperCamelCase         =						(
								    Path(a_					).parent.parent.parent.parent if self._path is None else Path(self._path					).parent.parent
								)
								_UpperCamelCase         =						path_to_transformer_root / "templates" / "adding_a_new_model"

								# Execute cookiecutter
								if not self._testing:
										cookiecutter(str(a_					)					)
								else:
										with open(self._testing_file							,				"""r"""					) as configuration_file:
												_UpperCamelCase         =						json.load(a_					)

										cookiecutter(
										    str(path_to_cookiecutter if self._path is None else self._path					)							,				no_input=a_							,				extra_context=a_							,				)

								_UpperCamelCase         =						[directory for directory in os.listdir() if "cookiecutter-template-" in directory[:22]][0]

								# Retrieve configuration
								with open(directory + """/configuration.json"""							,				"""r"""					) as configuration_file:
										_UpperCamelCase         =						json.load(a_					)

								_UpperCamelCase         =						configuration["lowercase_modelname"]
								_UpperCamelCase         =						configuration["generate_tensorflow_pytorch_and_flax"]
								os.remove(F'{directory}/configuration.json'					)

								_UpperCamelCase         =						"PyTorch" in generate_tensorflow_pytorch_and_flax
								_UpperCamelCase         =						"TensorFlow" in generate_tensorflow_pytorch_and_flax
								_UpperCamelCase         =						"Flax" in generate_tensorflow_pytorch_and_flax

								_UpperCamelCase         =						F'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
								os.makedirs(a_							,				exist_ok=a_					)
								os.makedirs(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}'							,				exist_ok=a_					)

								# Tests require submodules as they have parent imports
								with open(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py'							,				"""w"""					):
										pass

								shutil.move(
								    F'{directory}/__init__.py'							,				F'{model_dir}/__init__.py'							,				)
								shutil.move(
								    F'{directory}/configuration_{lowercase_model_name}.py'							,				F'{model_dir}/configuration_{lowercase_model_name}.py'							,				)

								def remove_copy_lines(a					):
										with open(a_							,				"""r"""					) as f:
												_UpperCamelCase         =						f.readlines()
										with open(a_							,				"""w"""					) as f:
												for line in lines:
														if "# Copied from transformers." not in line:
																f.write(a_					)

								if output_pytorch:
										if not self._testing:
												remove_copy_lines(F'{directory}/modeling_{lowercase_model_name}.py'					)

										shutil.move(
										    F'{directory}/modeling_{lowercase_model_name}.py'							,				F'{model_dir}/modeling_{lowercase_model_name}.py'							,				)

										shutil.move(
										    F'{directory}/test_modeling_{lowercase_model_name}.py'							,				F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py'							,				)
								else:
										os.remove(F'{directory}/modeling_{lowercase_model_name}.py'					)
										os.remove(F'{directory}/test_modeling_{lowercase_model_name}.py'					)

								if output_tensorflow:
										if not self._testing:
												remove_copy_lines(F'{directory}/modeling_tf_{lowercase_model_name}.py'					)

										shutil.move(
										    F'{directory}/modeling_tf_{lowercase_model_name}.py'							,				F'{model_dir}/modeling_tf_{lowercase_model_name}.py'							,				)

										shutil.move(
										    F'{directory}/test_modeling_tf_{lowercase_model_name}.py'							,				F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py'							,				)
								else:
										os.remove(F'{directory}/modeling_tf_{lowercase_model_name}.py'					)
										os.remove(F'{directory}/test_modeling_tf_{lowercase_model_name}.py'					)

								if output_flax:
										if not self._testing:
												remove_copy_lines(F'{directory}/modeling_flax_{lowercase_model_name}.py'					)

										shutil.move(
										    F'{directory}/modeling_flax_{lowercase_model_name}.py'							,				F'{model_dir}/modeling_flax_{lowercase_model_name}.py'							,				)

										shutil.move(
										    F'{directory}/test_modeling_flax_{lowercase_model_name}.py'							,				F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py'							,				)
								else:
										os.remove(F'{directory}/modeling_flax_{lowercase_model_name}.py'					)
										os.remove(F'{directory}/test_modeling_flax_{lowercase_model_name}.py'					)

								shutil.move(
								    F'{directory}/{lowercase_model_name}.md'							,				F'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md'							,				)

								shutil.move(
								    F'{directory}/tokenization_{lowercase_model_name}.py'							,				F'{model_dir}/tokenization_{lowercase_model_name}.py'							,				)

								shutil.move(
								    F'{directory}/tokenization_fast_{lowercase_model_name}.py'							,				F'{model_dir}/tokenization_{lowercase_model_name}_fast.py'							,				)

								from os import fdopen, remove
								from shutil import copymode, move
								from tempfile import mkstemp

								def replace(a							,				a							,				a					):
										# Create temp file
										_UpperCamelCase         =						mkstemp()
										_UpperCamelCase         =						False
										with fdopen(a_							,				"""w"""					) as new_file:
												with open(a_					) as old_file:
														for line in old_file:
																new_file.write(a_					)
																if line_to_copy_below in line:
																		_UpperCamelCase         =						True
																		for line_to_copy in lines_to_copy:
																				new_file.write(a_					)

										if not line_found:
												raise ValueError(F'Line {line_to_copy_below} was not found in file.'					)

										# Copy the file permissions from the old file to the new file
										copymode(a_							,				a_					)
										# Remove original file
										remove(a_					)
										# Move new file
										move(a_							,				a_					)

								def skip_units(a					):
										return (
										    ("generating PyTorch" in line and not output_pytorch)
										    or ("generating TensorFlow" in line and not output_tensorflow)
										    or ("generating Flax" in line and not output_flax)
										)

								def replace_in_files(a					):
										with open(a_					) as datafile:
												_UpperCamelCase         =						[]
												_UpperCamelCase         =						False
												_UpperCamelCase         =						False
												for line in datafile:
														if "# To replace in: " in line and "##" not in line:
																_UpperCamelCase         =						line.split("""\""""					)[1]
																_UpperCamelCase         =						skip_units(a_					)
														elif "# Below: " in line and "##" not in line:
																_UpperCamelCase         =						line.split("""\""""					)[1]
																_UpperCamelCase         =						skip_units(a_					)
														elif "# End." in line and "##" not in line:
																if not skip_file and not skip_snippet:
																		replace(a_							,				a_							,				a_					)

																_UpperCamelCase         =						[]
														elif "# Replace with" in line and "##" not in line:
																_UpperCamelCase         =						[]
														elif "##" not in line:
																lines_to_copy.append(a_					)

										remove(a_					)

								replace_in_files(F'{directory}/to_replace_{lowercase_model_name}.py'					)
								os.rmdir(a_					)










'''simple docstring'''






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 		lowerCamelCase	(   _A							):
	snake_case_   = ["image_processor", "tokenizer"]
	snake_case_   = "LayoutLMv3ImageProcessor"
	snake_case_   = ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")



	def __init__( self		,   a_=None		,   a_=None		,   **a_						):
			lowerCAmelCase  :     Union[str, Any]       						= None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
					    " instead."		,   a_		,   )
					lowerCAmelCase  :     Tuple       						= kwargs.pop("feature_extractor"						)

			lowerCAmelCase  :     str       						= image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
					raise ValueError("You need to specify an `image_processor`."						)
			if tokenizer is None:
					raise ValueError("You need to specify a `tokenizer`."						)

			super().__init__(a_		,   a_						)



	def __call__( self		,   a_		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = True		,   a_ = False		,   a_ = None		,   a_ = None		,   a_ = 0		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = False		,   a_ = False		,   a_ = False		,   a_ = False		,   a_ = True		,   a_ = None		,   **a_		,   ):
			# verify input
			if self.image_processor.apply_ocr and (boxes is not None):
					raise ValueError(
					    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."						)

			if self.image_processor.apply_ocr and (word_labels is not None):
					raise ValueError(
					    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."						)

			# first, apply the image processor
			lowerCAmelCase  :     str       						= self.image_processor(images=a_		,   return_tensors=a_						)

			# second, apply the tokenizer
			if text is not None and self.image_processor.apply_ocr and text_pair is None:
					if isinstance(a_		,   a_						):
							lowerCAmelCase  :     Union[str, Any]       						= [text]  # add batch dimension (as the image processor always adds a batch dimension)
					lowerCAmelCase  :     int       						= features["words"]

			lowerCAmelCase  :     List[Any]       						= self.tokenizer(
			    text=text if text is not None else features["words"]		,   text_pair=text_pair if text_pair is not None else None		,   boxes=boxes if boxes is not None else features["boxes"]		,   word_labels=a_		,   add_special_tokens=a_		,   padding=a_		,   truncation=a_		,   max_length=a_		,   stride=a_		,   pad_to_multiple_of=a_		,   return_token_type_ids=a_		,   return_attention_mask=a_		,   return_overflowing_tokens=a_		,   return_special_tokens_mask=a_		,   return_offsets_mapping=a_		,   return_length=a_		,   verbose=a_		,   return_tensors=a_		,   **a_		,   )

			# add pixel values
			lowerCAmelCase  :     Tuple       						= features.pop("pixel_values"						)
			if return_overflowing_tokens is True:
					lowerCAmelCase  :     Optional[int]       						= self.get_overflowing_images(a_		,   encoded_inputs["overflow_to_sample_mapping"]						)
			lowerCAmelCase  :     str       						= images

			return encoded_inputs



	def 	_lowerCamelCase     ( self		,   a_		,   a_						):
			# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
			lowerCAmelCase  :     Optional[Any]       						= []
			for sample_idx in overflow_to_sample_mapping:
					images_with_overflow.append(images[sample_idx]						)

			if len(a_						) != len(a_						):
					raise ValueError(
					    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
					    F''' {len(a_						)} and {len(a_						)}'''						)

			return images_with_overflow



	def 	_lowerCamelCase     ( self		,   *a_		,   **a_						):
			return self.tokenizer.batch_decode(*a_		,   **a_						)



	def 	_lowerCamelCase     ( self		,   *a_		,   **a_						):
			return self.tokenizer.decode(*a_		,   **a_						)



	@property
	def 	_lowerCamelCase     ( self						):
			return ["input_ids", "bbox", "attention_mask", "pixel_values"]



	@property
	def 	_lowerCamelCase     ( self						):
			warnings.warn(
			    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,   a_		,   )
			return self.image_processor_class





	@property
	def 	_lowerCamelCase     ( self						):
			warnings.warn(
			    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,   a_		,   )
			return self.image_processor



